The subject matter disclosed herein relates generally to imaging systems, and more particularly, to a method and apparatus for reconstructing an image using iterative techniques.
Traditionally, images have been reconstructed from Computed Tomography (CT) data using direct reconstruction algorithms such as filtered back projection (FBP) or convolution back projection (CBP). Recently, iterative reconstruction (IR) algorithms have again been assessed for use in the reconstruction of CT images. One advantage of IR algorithms is that IR algorithms can more accurately model the measurements obtained from CT systems. This is particularly true for helical CT systems with multi-slice detectors because helical CT systems produce projection measurements that pass obliquely through the two-dimensional (2D) reconstructed image planes. By more accurately modeling these projections, IR algorithms can produce reconstructions with higher quality, lower noise, and fewer artifacts.
However, a major challenge of IR is the computation time and computational resources required to complete a reconstruction. More specifically, because IR has been studied for other types of reconstruction problems, a variety of methods have been proposed for computing the solution to the cost function. Some of these methods include ordered subset expectation maximization (OSEM), preconditioned conjugate gradient (PCG), and iterative coordinate descent (ICD). All of these methods perform the minimization required in equation (1) (shown below) by optimizing an objective function. However, each of the described methods may require a relatively high number of iterations to achieve the final answer. As a result, the conventional IR techniques may require a relatively large amount of time to reconstruct an image.